MediAI: AI-Driven Healthcare Revolution for Smarter Diagnostics and Treatment

Abstract

Artificial Intelligence (AI) has been a game-changer in modern healthcare, making medical treatments far more accurate, efficient, and tailored to each patient. This study examines the diverse applications of artificial intelligence in critical healthcare sectors, including medical imaging, disease prediction, pharmaceutical development, robotic-assisted procedures, and patient management. We used strong data sets such as the NIH chest X-ray dataset, MIMIC-III clinical records, UK Biobank, and DrugBank, and we used several machine learning models, such as CNNs, LSTMs, Generative Adversarial Networks (GANs), and Reinforcement Learning algorithms. Our research shows that CNN-based diagnostic tools were more accurate than standard imaging approaches, with an accuracy rate of up to 97 percent in identifying diseases. Using LSTMs for predictive analytics cut down on false positives by more than 50 percent when predicting the likelihood of chronic illnesses. GAN-based models improved the prediction of drug-target interactions by 30 percent, which sped up the process of making new drugs. AIpowered robotic-assisted surgical devices had a 96 percent success rate, which meant fewer complications and shorter hospital stays. The study examines not only technological performance but also significant ethical and regulatory issues like data privacy, bias of algorithms, and the opacity of AI decision-making processes. We stress the importance of explainable AI (XAI), federated learning, and standardised ethical frameworks to help people use AI in a responsible way. The main contributions of this paper include a comparison of how well AI models work on different healthcare jobs, a way to combine different medical datasets, and suggestions for how to deploy AI in a way that is scalable and ethical. This research highlights AI's potential to enhance, rather than replace, clinical expertise by integrating technological insights with practical applications, resulting in smarter diagnostics, better patient outcomes, and more resilient healthcare systems globally.

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